You are working on a binary classification problem with 3 input features and have chosen to apply a bagging algorithm (Algorithm X) on this data. You have set max_features = 2 and n_estimators = 3. Each estimator has an accuracy of 70%. Algorithm X aggregates the results of individual estimators based on maximum voting. I get a solution. The maximum accuracy you can get is 79.85% (rounded to two decimal places). Here’s how to calculate it: The probability of a single estimator being wrong is 30%. The probability of all three estimators being wrong is 0.3 * 0.3 * 0.3 = 0.027. Therefore, the probability of at least one estimator being correct is 1 - 0.027 = 0.973. The probability of all three estimators being correct is 0.7 * 0.7 * 0.7 = 0.343. Therefore, the probability of at least two estimators being correct is 3 * 0.7 * 0.7 * 0.3 = 0.441. The probability of all three estimators being correct or at least two estimators being correct is 0.343 + 0.441 = 0.784. Finally, the probability of the majority vote being correct is 0.784 + 0.5 * 0.027 = 0.7985. Therefore, the maximum accuracy you can get is 79.85% (rounded to two decimal places). Is it correct? If yes, then can anyone explain 0.343 + 0.441 = 0.784.

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